In this work, we attempt the task of sarcasm detection in online discussion forums. Particularly, we propose
a hybrid network, named CASCADE, that utilizes both content and contextual-information required for sarcasm detection. It starts by processing contextual information in two ways. First, it performs user profiling to create user embeddings that capture indicative behavioral traits for sarcasm. Recent findings suggest that such modeling of the user and their preferences, is highly effective for the given task [1]}. It makes use of users' historical posts to model their writing style (stylometry) and personality indicators, which are then fused into comprehensive user embeddings using a multi-view fusion approach, Canonical Correlation Analysis (CCA). Second, it extracts contextual information from the discourse of comments in the discussion forums. This is done by document modeling of these consolidated comments belonging to the same forum. We hypothesize that these discourse features would give the important contextual information, background cues along with topical information required for detecting sarcasm.
